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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yanlei Mezrag, Lydia Sun, Xingzhi Xu, Charles Macdonald, Kincaid Bhaskar, Dhananjay Krishnaswamy, Smita Wolf, Guy Rieck, Bastian Université de Montréal Dept. of Math. & Stat Canada Mila - Quebec AI Institute MontréalQC Canada Yale University Dept. of Comp. Sci. United States Dept. of Genetics United States Applied Mathematics Program New HavenCT United States Université de Fribourg Department of Informatics FR Fribourg Switzerland Helmholtz Zentrum München Institute of AI for Health BY Munich Germany

ISBN: (纸本)9798350368741

The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datasets. A common method in manifold learning consists in hypothesizing that datasets lie on a lower dimensional manifold. This allows to study the geometry of point clouds by extracting meaningful descriptors like curvature. In this work, we will present Adaptive Local PCA (AdaL-PCA), a data-driven method for accurately estimating various notions of intrinsic curvature on data manifolds, in particular principal curvatures for surfaces. The model relies on local PCA to estimate the tangent spaces. The evaluation of AdaL-PCA on sampled surfaces shows state-of-the-art results. Combined with a PHATE embedding, the model applied to single-cell RNA sequencing data allows us to identify key variations in the cellular differentiation. © 2025 IEEE.

关键词： Gaussian curvature Principal curvature principal directions single-cell

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Principal Curvatures Estimation with Applications to Single Cell Data

Principal Curvatures Estimation with Applications to Single ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yanlei Zhang Lydia Mezrag Xingzhi Sun Charles Xu Kincaid Macdonald Dhananjay Bhaskar Smita Krishnaswamy Guy Wolf Bastian Rieck Dept. of Math. & Stat. Université de Montréal Mila – Quebec AI Institute Montréal CA Dept. of Comp. Sci. Yale University Dept. of Genetics Applied Mathematics Program New Haven CT USA Helmholtz Zentrum München Institute of AI for Health Munich BY DE Department of Informatics Université de Fribourg Fribourg CH

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Manifolds Sequential analysis Adaptation models Data analysis RNA Transcriptomics Curve fitting Speech processing Tuning Principal component analysis

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TWO ALGORITHMS FOR LC^1 UNCONSTRAINED OPTIMIZATION

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Journal of Computational mathematics 2000年第6期18卷 621-632页

作者： Wen-yu Sun R.J.B.de Sampaio Jin-Yun Yuan School of Mathematics and Computer Science Nanjing Normal UniversityNanjing 210097China Applied Informatics Program Pontifical Catholic University of ParanaCuritibaPR81611-970Brazil Department of Mathematics Federal University of ParanaCuritibaPR81531-990Brazil

Presents two algorithms for LC unconstrained optimization problems which use the second order Dini upper directional derivative. Simplicity of the methods to use and perform; Discussion of related properties of the it... 详细信息

关键词： nonsmooth optimization directional derivative Newton-like method convergence trust region method

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Principal Curvatures Estimation with Applications to Single Cell Data

arXiv

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arXiv 2025年

作者： Zhang, Yanlei Mezrag, Lydia Sun, Xingzhi Xu, Charles Macdonald, Kincaid Bhaskar, Dhananjay Krishnaswamy, Smita Wolf, Guy Rieck, Bastian Université de Montréal Dept. of Math. & Stat. QC Montréal Canada Mila – Quebec AI Institute MontréalQC Canada Yale University Dept. of Comp. Sci. New HavenCT United States Dept. of Genetics New HavenCT United States Applied Mathematics Program New HavenCT United States Université de Fribourg Department of Informatics Fribourg Switzerland Helmholtz Zentrum München Institute of AI for Health Munich Germany

关键词： Spatio-temporal data

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A New Hybrid Algorithm of Bisection and Modified Newton's Method for the nth root-finding of a Real Number 10

A New Hybrid Algorithm of Bisection and Modified Newton's Me...

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2020 10th International Conference on applied Physics and mathematics, ICAPM 2020

作者： Pinkham, S. Sansiribhan, S. Informatics Mathematics Program Faculty of Science and Technology Suan Sunandha Rajabhat University 10300 Thailand Applied Physic Program Faculty of Science and Technology Suan Sunandha Rajabhat University 10300 Thailand

This paper presents a new algorithm to approximate the nth root of a given real number z. The proposed algorithm is a hybrid algorithm between the Bisection method and the combination of the inverse of sine series and Newton's method. The proposed algorithm will be tested to find the numerical results on Matlab programming. The results showed that if z > 1, the proposed method converges with the initial interval [1, z] and if 0 © Published under licence by IOP Publishing Ltd.

关键词： Newton-Raphson method

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An algorithm based on Voronoi diagrams for the multi-stream multi-source multicast routing problem

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International Journal of Innovative Computing and Applications 2018年第4期9卷 216-229页

作者： De Andrade, Romerito Campos Goldbarg, Marco César Goldbarg, Elizabeth Ferreira Gouveâ Postgraduate Program in Systems and Computation Federal University of Rio Grande Do Norte Campus Universitário Lagoa Nova Natal RN Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande Do Norte Campus Universitário Lagoa Nova Natal RN Brazil

In this study, we present a new heuristic for the multi-stream multi-source multicast routing problem. The core of the heuristic proposed in this study is based on a generalisation of Voronoi diagrams in graphs. It allows building the trees needed to serve the demands of multiple sessions efficiently. Also, the proposed algorithm supports multiple sources. We performed an extensive experimental analysis of different network and problem configurations such as the number of sessions, nodes, sources and participants per session. We compare the proposed algorithm to heuristics proposed previously. The results of the experiments showed that the heuristic proposed in this study finds high-quality solutions efficiently. © 2018 Inderscience Enterprises Ltd.

关键词： Multicasting

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Bibliometric Analysis: Research Trends in Leaf Image Segmentation and Classification

Bibliometric Analysis: Research Trends in Leaf Image Segment...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Agustini, Eka Puji Gernowo, Rahmad Kurnianingsih Wibowo, Adi Warsito, Budi Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Doctoral Program of Information System Diponegoro University Semarang Indonesia Master of Applied Science Program Polytechnic Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia Faculty of Science and Mathematics Diponegoro Universitty Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350350524

Exploring various phenomena and issues related to leaf images is paramount, particularly in segmentation and classification of such images. This study employs bibliometric analysis to delve into two overarching themes: The trends in publication and the evolution of publications, along with a keyword-based analysis. The research methodology unfolds in two stages: (1) data collection and (2) analysis. The dataset comprises 1,248 articles sourced from the Scopus database, covering the period from 1988 to 2023. The research findings unveil a noteworthy surge in publication trends, peaking at 231 documents in 2023. An in-depth examination of journal names demonstrates that studies on the segmentation and classification of leaf images are predominantly featured in computer science journals, exemplified by the publication of 589 documents. Furthermore, an analysis of frequently used keywords highlights 'Extraction' as the predominant term, employed a total of 364 times. This underscores that the research focus on leaf image segmentation and classification presents ample opportunities for researchers to delve more profoundly into the subject. © 2024 IEEE.

关键词： Image segmentation

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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applied mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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Fuzzy-Valued Distance Between Fuzzy Numbers Based on a Generalized Extension Principle 19th

Fuzzy-Valued Distance Between Fuzzy Numbers Based on a Gen...

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19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022

作者： Araújo, Juscelino Bedregal, Benjamin Santiago, Regivan Postgraduate Program in Systems and Computation Federal University of Rio Grande do Norte Natal59072-970 Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Natal59072-970 Brazil

ISBN: (纸本)9783031089701

This paper presents a generalization of the extension principle for fuzzy numbers. The minimum is substituted by a general binary aggregation function. It is used to extend the usual metric for real numbers to fuzzy numbers, generating a new family of fuzzy-valued distances between fuzzy numbers. Then, some conditions on these aggregation functions are studied to hold the fuzzy number properties of the generated distances. © 2022, Springer Nature Switzerland AG.

关键词： Fuzzy rules

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Combining different ways to generate diversity in bagging models: An evolutionary approach

Combining different ways to generate diversity in bagging mo...

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International Joint Conference on Neural Networks (IJCNN)

作者： Diego S. C. Nascimento Anne M. P. Canuto Lígia M. M. Silva André L. V. Coelho Informatics and Applied Mathematics Department Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil Graduate Program in Applied Informatics University of Fortaleza-UNIFOR Fortaleza Ceara Brazil

Bagging algorithm has been proven to be effective when dealing with on different classification problems. However, the success of Bagging depends strongly on the diversity level reached by the individual classifiers of the ensemble models. Diversity in ensemble can be obtained when the individual classifiers are built using different circumstances, such as parameter settings, training datasets and learning algorithms. This paper presents a new approach which combines these three different ways to obtain high diversity in Bagging models, aiming, as a consequence, to obtain high levels of accuracy for the ensembles. In the proposed approach, in order to obtain the optimal configurations of features and classifiers in Bagging models, we have applied an evolutionary approach composed of two genetic algorithm instances. In order to validate the proposed approach, experiments involving 10 classification algorithms have been conducted, applying the resulting Bagging structures in 5 pattern classification datasets taken from the UCI repository. In addition, we analyze the performance of the resulting Bagging structures in terms of two recently proposed diversity measures, referred to as good and bad.

关键词： Diversity reception Genetic algorithms Bagging Accuracy Training Biological cells Correlation

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